Geometric interpretation of an LP’s objective function
Identifying whether a shape/set is convex or not
Knowing runtime guarantees for LP solving
Knowing why the feasible region of an LP is always convex
LP modeling
Knowing whether a constraint is modelable in a linear program
Sketching feasible regions for an LP and determining whether it is feasible, infeasible, or unbounded, based on the feasible region and the objective function direction
Being able to understand how the LP model of a problem we have seen in class, such as max flow, will be modified by the introduction of new elements (see HW 1 problem 18)
LP integrality
Knowing whether it is sufficient to have all integer A, b, c to have a guarantee of obtaining an all-integer optimal solution for an LP
Understanding whether modifications to an existing problem preserve integrality (see HW 1 problem 18)
Runtime complexity analysis
Being able to determine big-O time complexity for a Python function